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OPTIMIZATION AND KINETIC MODEL FOR TRANSESTERIFICATION OF
WASTE COOKING OIL USING TUNGSTOPHOSPHORIC ACID CATALYST
AMIN TALEBIAN KIAKALAIEH
A report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Science (Chemical Engineering)
Faculty of Chemical Engineering
Universiti Teknologi Malaysia
MARCH 2012
v
TO MY BELOVED WIFE,
MY LOVING MOTHER AND MY BROTHER
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ACKNOWLEDGEMENT
Praises to Allah for giving me the strength, perseverance and intention to go
through and complete my study.
I would like to express my sincere gratitude to my respected supervisor Prof.
Nor Aishah Saidina Amin for her help, support, and guidance. I owe her a lot for
what she taught me during these years.
Last but not least, I am grateful to my loving wife, mother, father and mother
in law and Mr. Jafar Jalayeri for their loves and supports.
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ABSTRACT
Transesterification of waste cooking oil with heterogeneous
tungstophosphoric acid (TPA) catalyst and methanol was investigated. Response
Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed
to study the relationships of process variables on free fatty acid conversion and for
predicting the optimal conditions. The highest conversion was 88.6% at optimum
reaction conditions of 65 ˚C reaction temperature, 70:1 molar ratio of methanol to
oil, 10 %wt catalyst amount, and 14 h reaction time. The RSM and ANN could
accurately predict the experimental results, with R2 = 0.9987 and R2= 0.985,
respectively. The TPA catalyst exhibited good potential as a stable and active
catalyst over four time’s reusability. The reaction followed first-order kinetics and
the calculated activation energy was Ea= 53.99 kJ/mol while the pre-exponential
factor was A= 2.9x107 min-1. According to the biodiesel characterization results
(ASTM D6751) the product of this study has high potential for using as a substitute
for conventional catalyst in biodiesel production.
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ABSTRAK
Transesterifikasi minyak masak terpakai dengan mangkin heterogen
tungstophosphoric acid (TPA) dan metanol telah dikaji. Metodologi permukaan
bertindakbalas (RSM) dan rangkaian neural tiruan (ANN) digunakan untuk mengkaji
hubungan antara pemboleh ubah proses dan penukaran asid lemak bebas dan untuk
mengkaji parameter optimum. Penukaran yang paling tinggi ialah dengan kadar 88.6
peratus, dengan keadaan optimum 14 jam masa tindak balas, suhu tindak balas 65
darjah Celcius, nisbah molar methanol dan minyak 70:1 dan 10% kepekatan
mangkin. RSM dan ANN boleh meramal keputusan eksperimen dengan tepat; R2
bagi RSM ialah 0.9987 dan R2 bagi ANN ialah 0.985. Mangkin TPA mempunyai
potensi tertinggi sebagai mangkin yang aktif dan stabil dan boleh digunakan berulang
kali. Tindak balas ini mematuhi kinetik tertib pertama dan tenaga pengaktifan ialah
Ea = 53.99 kJ/mol; factor pra-eksponen sama dengan A= 2.9x107 min-1. Kajian ini
dapat menghasilkan proses penghasilan biodisel dengan minyak masak terbuang
dengan mangkin TPA heterogen yang mesra alam dan menepati piawaian ASTM
D6751.
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TABLES OF CONTENTS
CHAPTER CONTENT PAGE
TITLE I
DECLERATION II
DEDICATION ΙΙΙ
ACKNOWLEDGMENT IV
ABSTRACT V
ABSTRAK VI
TABLES OF CONTENTS VII
LIST OF TABLES XI
LIST OF FIGURES XIII
LIST OF ABBREVIATIONS XV
LIST OF SYMBOLS XVI
LIST OF APPENDICES XVII
1 INTRODUCTION 1
1.1 Background of research 1
1.2 Problem statement 5
1.3 Objectives 6
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1.4 Scopes 7
2 LITERATURE REVIEW 8
2.1 History 8
2.2 Vegetable oils 9
2.3 Chemical compositions 12
2.4 The biodiesel production from waste cooking oil 13
2.5 Transesterification reaction 15
2.6 The catalytic homogeneous transesterification reaction 16
2.6.1 Alkali catalysts 16
2.6.1.1 The pretreatment of WCO 18
2.6.2 Acid catalysts 20
2.7 The catalytic heterogeneous reaction 20
2.7.1 Solid acid catalysts 21
2.7.2 Solid base catalysts 26
2.8 Enzymatic catalysts 28
2.9 The non-catalyzed transesterification reaction 31
2.10 Kinetic modeling 33
2.11 Optimization 36
2.12 Fuel properties 39
2.12.1 Density 40
2.12.2 Flash point 40
2.12.3 Cetane number 40
2.12.4 Heating value 41
2.12.5 Carbon residue 41
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3 METHODOLOGY 43
3.1 Research methodology 43
3.2 Material and equipments 45
3.3 Tungstophosphoric acid (TPA) catalyst 46
3.4 Process optimization 47
3.4.1 Transesterification reaction procedure 47
3.4.2 Experimental design 48
3.5 Kinetic parameters determination 49
3.6 Biodiesel characterization 52
3.6.1 Density (ASTM D4052) 52
3.6.2 Kinematic viscosity (ASTM D445) 53
3.6.3 Water content (ASTM D2709) 55
3.6.4 Flash point (ASTM D93) 56
3.6.5 Pour point (ASTM D97) 58
3.6.6 Cloud point (ASTM D2500) 59
3.6.7 Acid value (ASTM D664) 61
4 RESULTS AND DISCUSSION 62
4.1 Optimization of WCO transesterification reaction 62
4.1.1 Response surface methodology (RSM) 62
4.1.2 Artificial neural network (ANN) 67
4.1.3 Comparison between RSM and ANN models 72
4.1.4 Effect of reaction parameters on transesterification reaction 72
4.2 Catalyst recycling and reusability 78
4.3 Kinetic modeling 79
4.4 Biodiesel characterization 84
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4.4.1 Observation of biodiesel 84
4.4.2 Physical properties of biodiesel 85
5 CONCLUSION AND RECOMMENDATION 87
5.1 Conclusion 87
5.2 Recommendation 89
REFRENCES 90
APPENDIX A 113
APPENDIX B 114
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LIST OF TABLES
TABLE NO TITLE PAGE
1.1 Different feedstock for production of biodiesel 4
2.1 The properties of different vegetable oils 10
2.2 The physical and chemical properties of vegetable oil methyl ester 11
2.3 The structure of a typical triglyceride molecule 12
2.4 The chemical structure of fatty acids 13
2.5 Possible FFA content for Alkali catalyzed transesterification 17
2.6 The final yields and acid value for four types of solid acid catalyst 24
2.7 Advantages and disadvantages of the acid/base catalysts
tested for transesterification 28
2.8 The different kinds of microbial lipases 30
2.9 The different types of methods for biodiesel production 33
2.10 The major parameters for quality of biodiesel 42
3.1 The waste cooking oil chemical and physical properties 45
3.2 Experimental level coded and range of independent parameters 49
4.1 The run numbers and experimental and RSM predicted results 53
4.2 The results for ANOVA test 65
4.3 The ANN weights and biases for input and output layers 70
4.4 Predicted conversion (%) by RSM and ANN models 71
4.5 The reaction rate constants in different temperatures 83
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4.6 Properties of biodiesel compared with ASTM standards 86
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LIST OF FIGURES
FIGURE NO TITLE PAGE
1.1 World energy supplies 2
2.1 The feed stock and the numbers of production sites in U.S.A 11
2.2 The schematic flow diagram of the bio-diesel production
from used cooking oil 14
2.3 The general equation of transesterification reaction 15
2.4 The methanolysis of triglyceride 16
2.5 Chemical mechanism of heterogeneous acid catalyst 22
2.6 Chemical mechanism of heterogeneous base catalyst 26
3.1 Research methodology flow diagram 44
3.2 The structure of TPA catalysts 46
3.3 The equipments for finding density 53
3.4 The kinematic viscosity determination equipment 54
3.5 The water content determination equipment 55
3.6 Open cup method for flash point determination 57
3.7 The pour and cloud point determination equipment 60
4.1 The RSM plot of predicted versus actual conversion (%) 66
4.2 The RSM normal plot of residuals 66
4.3 Optimum number of hidden neurons (lowest MSE) 68
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4.4 Optimum number of hidden neurons (R-square) 68
4.5 The multilayer full feed forward neural network topology
for the estimation of FAME conversion 69
4.6 Effect of reaction temperature and time on reaction conversion 73
4.7 Effect of molar ratio of methanol to oil and reaction time on
on reaction conversion 74
4.8 Effect of catalyst amount and reaction time on reaction
conversion 75
4.9 Effect of molar ratio of methanol to oil and reaction
temperature on reaction conversion 76
4.10 Effect of reaction temperature and catalyst amount
on reaction conversion 77
4.11 Effect of catalyst amount and molar ratio of methanol to oil
on reaction conversion 78
4.12 Number of times for catalyst reusability 79
4.13 Effect of reaction temperature on conversion of biodiesel
(molar ratio of methanol to oil = 70:1,catalyst amount = 10 w%) 80
4.14 Reaction rate constant 82
4.15 Arrhenius equation Ln k' versus 1/T*103 84
4.16 Final product before separation 85
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LIST OF ABBREVIATIONS
ABE Antivated Bleaching Earth
ANN Artificial Neural Network
COME Canola Oil Methyl Ester
DG Diglycerides
EBP End Boiling Point
E Ester
FAME Fatty Acid Methyl Ester
FFA Free Fatty Acid
GL Glycerol
KF Potassium Fluoride
KOME Karanja Oil Methyl Ester
MG Mono Glycerides
PAH Polycyclic Aromatic Hydro
PKOME Palm Kernel Oil Methyl Ester
PPOME Palm Oil Methyl Ester
PME Palm Oil Methyl Ester
RSM Response Surface Methodology
ROME Rice Bran Oil Methyl Ester
SOME Soybean Oil Methyl Ester
SFOME Sunflower Oil Methyl Ester
TG Triglyceride
THF Tetra Hydro Furan
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LIST OF SYMBOLS
R2adj Adjust R-Square
Ea Activation Energy (kJ/mol)
β 0 Constant Coefficient
X Conversion (%)
ε Error
R Gas Constant (m3. Pa/ K. mol)
CA0 Initial Concentration
A Pre-Exponential Factor
ypi Predicted Out Put Values
η Response
R2 R-Square
k Reaction Rate Constant (1/min)
ra Reaction Rate
t Time (h)
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LIST OF APPENDICES
APPENDIX TITLE PAGE
A Experimental Calculation 124
B ASTM Methods for biodiesel characterization 125
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1
1 CHAPTER 1
2 INTRODUCTION
1.1 Background of Research
Energy consumption is inevitable for human existence. There are various
reasons for the search of an alternative fuel that is technically feasible,
environmentally acceptable, economically competitive, and readily available. The
first and most important reason is the increasing demand for fossil fuels in all sectors
of human life, be it transportation, power generation, industrial processes, and
residential consumption. This increased demand gives rise to environmental concerns
such as increased CO2 emission, production of greenhouse gases, and global
warming. World energy consumption doubled between 1971 and 2001 (Figure 1.1)
and the world energy demand will increase 53% by the year 2030. For instance,
petroleum consumption will rise from 84.4 to 116 million barrels per day in USA
until year 2030 (Bioengineering Resource; Energy Department).
2
Figure 1.1 World energy supplies
The second reason is that fossil fuel resources are non renewable, and they
will be exhausted in the near future. Some reports show that oil and gas reserves will
be depleted in 41 and 63 years, respectively, if the consumption pace remain constant
(Reports and publications). The last reason is the price instability of fuels such as
crude oil, which is a serious threat for countries with limited resources. Several
alternatives such as wind, solar, hydro, nuclear, bio-fuel, and biodiesel have been
suggested but all of them are still in the research and development stage.
The inventor of biodiesel engines, Rudolf Christian Karl Diesel (1858-1913)
demonstrated the use of vegetable oils as a substitute for diesel fuel in the 19th
century (Orchad et al., 2007). He believed the utilization of biomass fuel will
becomes a reality as future versions of his engine are designed and developed.
Biodiesel is a mono alkyl ester of fatty acids produced from vegetable oils or
animal fats. In other words, when a vegetable oil or animal fat chemically reacts with
an alcohol, it can produce Fatty Acid Methyl Ester (FAME); a vegetable oil can be
used in diesel engines after some adjustments and modifications. Vegetable oils
3
contain saturated hydrocarbons (triglycerides) which consist of glycerol and esters of
fatty acids. In addition, fatty acids have different numbers of bonds and carbon chain
lengths. There are different kinds of modification methods, such as dilution, thermal
cracking (pyrolysis), transesterification, and microemulsification. However,
transesterification is the best method for producing higher quality biodiesel (Meher et
al., 2006; Knothe, 2001; Sinha et al., 2008; Ramadhas et al., 2004; Sharma and
Singh, 2008).
All fatty acid sources such as animal fats or plant lipids (more than 300 types
of them) can be used in biodiesel production (Basha et al., 2009; Canoira et al.,
2006; Keshin et al., 2008). Table 1.1 show various feedstock’s in biodiesel
production (Ejas and Younis, 2011). The utilization of these types of sources has
given rise to certain concerns as some of them are important food chain materials
(Pimentel et al., 2009; Srinivasan, 2009). In other words, the production of bio-fuels
from human nutrition sources can cause a food crisis. Therefore, the majority of
researchers have used non-edible oils or waste edible oils as feedstock for biodiesel
production. The most important obstacle in biodiesel industrialization and
commercialization is production costs. Therefore, the usage of waste edible oils can
reduce biodiesel production costs by 60% to 90% (Canakci and Van Gerpen, 2001;
Zhang et al., 2003b; Kulkarni and Dalai, 2006; Haas et al., 2006; Marchetti et al.,
2008).
4
Table 1.1 Different feedstock for production of biodiesel
Biodiesel has a significant influences in reduction of engine emission is such
as unburned hydrocarbons (68%), particulars (40%), carbon monoxide (44%), sulfur
oxide (100%), and polycyclic aromatic hydrocarbons (PAH) (80-90 %) (Tyson and
McCormic, 2006). Meanwhile, it is safer to store and handle and it can be easily
produced in domestic quantities.
The investigation of vegetable oils as fuel started in 1978 and 1981 in the
United State and South Africa respectively. In 1982, methyl ester was produced in
Germany and Austria from rapeseed oil and a small pilot plant was built in Austria at
1985. Commercial production of methyl ester first began in Europe in 1990. The
annual production of biodiesel fuel in Europe was 1,210,000 tons in year 2000 and
world vegetable oil extraction increased from 56 million tons in 1990 to 88 million
tons in 2000 (Demirbas, 2008a).
Conventional feedstock Non-conventional feedstock
Mahua Soybean LardNile tilapia Rapeseed Tallow
Palm Canola Poultry fatpoultry Babassu Fish oil
Tobacco seed Brassica carinata BacteriaRubber plant Brassica napus Algae
Rice bran Copra FungiSesame Groundnut Micro algae
Sunflower Cynara cardunculus TarpenesBarley Cottonseed Latexes
Coconut Pumpkin MicroalgaeCorn Jojoba oil Pongamina pin nata
Used cooking oil Camelina Palangalinseed Peanut Jatropha curcasMustard Olive Sea mango
5
1.2 Problem statement
The biodiesel production cost highly dependent to the raw material price.
Some vegetable oil such as soybean, rapeseed and sunflower are the major
feedstocks for biodiesel production but the product is high price biodiesel definitely.
The usage of waste cooking oil in this research is a key component for reducing the
cost of biodiesel production around 60 % to 90 %.
Many researchers have used alkali catalysts (NaOH, KOH, CH3ONa) for
production of biodiesel because they are cheap and readily available. However, this
method has some limitations such as high energy consumption which in turn causes a
dramatic increase in capital equipment costs and safety issues. In addition, this
process is highly sensitive to water and free fatty acid (FFA) content in the feed
stock. This is because high water content can change the reaction to saponification
which causing reductions of ester yield, difficult separation of glycerol from methyl
ester, increase in the viscosity, and the formation of emulsion (Liu, 1994; Basu and
Norris, 1996). Therefore, the heterogeneous solid catalyst is a key component for
covering all these problems by simultaneous transesterification and esterification
reactions.
There is not any kinetic report for application of heteropoly acid as a catalyst
for biodiesel production from WCO. Most importantly, the applications of
heterogeneous catalysts have been reported by a lot of researchers recently.
Therefore, the kinetic parameters are key factor for simulation and particularly
economical industrialization of biodiesel production.
Costly lubrication equipment and time consuming tests are necessary to
conduct the optimization of the parameters involved in the transesterification
6
reaction. Therefore, it is of interest to implement some type of simulation and
modeling process to accurately predict the biodiesel conversion or yield evolution for
various initial conditions of reaction parameters. Thus, software’s such as Response
Surface Methodology (RSM) and Artificial Neural Network (ANN) can reduce or
eliminate a large number of laboratory tests and associated costs.
1.3 Objectives
1) To produce biodiesel from waste cooking oil at the presence of
heterogeneous solid acid catalyst.
2) To characterize the biodiesel chemical and physical properties according to
ASTM D6751 method.
3) To optimize and model the biodiesel production by response surface
methodology (RSM) and artificial neural network (ANN).
4) To find kinetic parameters for transesterification reaction of biodiesel
production by experimental method.
7
1.4 Scopes
In this study, Biodiesel produced from WCO at the presence of
tungstophosphoric acid (TPA) as catalyst in a batch-type reactor. Various reaction
parameters which include of molar ratio of alcohol to oil, reaction temperature,
reaction time, and catalyst concentration investigated to obtain the optimum
conditions.
The final product was characterized by ASTM D4052, ASTM D445, ASTM
D97, ASTM D2500, ASTM D93, and ASTM D2709 methods to comprise the
physical and chemical properties of biodiesel by ASTM D6751. In addition, the
researcher used Response Surface Methodology (RSM) and Artificial Neural
Network (ANN) for optimization and modeling of transesterification reaction
process. Meanwhile, researcher found the kinetic parameters (reaction rate constant
(k), activation energy (Ea), and pre-exponential factor (A)) by experimental method
at optimum reaction condition.
90
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